Aim: Habitat selection is a behavioural mechanism by which animals attempt to maximize their inclusive fitness while balancing competing demands, such as finding food and rearing offspring while avoiding predation, in a spatially and temporally heterogeneous environment. Different habitat characteristics may be associated with each of these demands, implying that habitat selection varies depending on the behavioural motivations of the animal. Here, we investigate behaviour specific habitat selection in African elephants and discuss its implications for distribution modelling and conservation.Location: Northern Botswana, Africa, case study. Methods:We use Bayesian state-space models to characterize location time-series data of elephants into two behavioural states (encamped and exploratory). We then develop habitat selection models for each behavioural state and contrast them to models based on data pooled among behaviours.Results: Spatial predictions of habitat use were often markedly different among the models. Behaviourspecific and pooled habitat selection models differed in model structure, the magnitude of model coefficients, and the form of the selection curve (linear or quadratic). Selection was typically strongest in the behaviour-specific models, though this varied according to behavioural state and habitat covariate. Main conclusions:Ignoring behavioural states often had important consequences for quantifying habitat selection. Quantifying selection irrespective of behaviour (among all behaviours) can obscure important species-habitat relationships, thereby risking weak or incorrect inferences. Behaviour-specific habitat selection provides greater insight into the process of habitat selection and can improve predictive habitat selection estimates. As some behaviours are more relevant to specific conservation objectives than others, focusing on behaviour-specific selection could improve how habitats are prioritized for conservation or management.

Managing multiple parks, reserves, and conservation areas collectively as conservation networks is a recent, yet growing trend. But in order for these networks to be ecologically viable, the functional connectivity of the landscape must be ensured. We assessed the connectivity between six African savanna elephant populations in southern Africa to test whether existing conservation networks were functioning and to identify other areas that could benefit from being managed as conservation networks. We used resource selection function models to create an index of habitat selection by males and female elephants. We employed this habitat use index as a resistance surface, and applied circuit theory to assess connectivity between adjacent elephant populations within six clusters of protected areas across southern Africa. Circuit theory current flow maps predicted a high likelihood of connectivity in the central portion of our study area (i.e. between the Chobe, Kafue, Luangwa, and Zambezi cluster). Main factors limiting connectivity across the study area were high human density in the east and a lack of surface water in the west. These factors effectively isolate elephants in the Etosha cluster in Namibia and Niassa clusters in Mozambique from the central region. Our models further identified two clusters where elephants might benefit from being managed as part of a conservation network, 1) northern Zambia and Malawi and 2) northern Mozambique.We conclude that using habitat selection and circuit theory models to identify conservation networks is a data-based method that can be applied to other focal species to identify and conserve functional connectivity.

Access management is among the most important conservation actions for grizzly bears in North America. In Alberta, Canada, nearly all grizzly bear mortalities are caused by humans and occur near roads and trails. Consequently, understanding how bears move relative to roads is of crucial importance for grizzly bear conservation. We present the first application of step-selection functions to model habitat selection and movement of grizzly bears. We then relate this to a step-length analysis to model the rate of movement through various habitats. Grizzly bears of all sex and age groups were more likely to select steps closer to roads irrespective of traffic volume. Roads are associated with habitats attractive to bears such as forestry cutblocks, and models substituting cutblocks for roads outperformed road models in predicting bear selection during day, dawn, and dusk time periods. Bear step lengths increased near roads and were longest near highly trafficked roads indicating faster movement when near roads. Bear selection of roads was consistent throughout the day; however, time of day had a strong influence over selection of forest structure and terrain variables. At night and dawn, bears selected forests of intermediate age between 40 and 100 yr, and bears selected older forests during the day. At dawn, bears selected steps with higher solar radiation values, whereas, at dusk, bears chose steps that were significantly closer to edges. Because grizzly bears use areas near roads during spring and most human-caused mortalities occur near roads, access management is required to reduce conflicts between humans and bears. Our results support new conservation guidelines in western North America that encourage the restriction of human access to roads constructed for resource extraction.

Resource selection function (RSF) models are commonly used to quantify species/habitat associations and predict species occurrence on the landscape. However, these models are sensitive to changes in resource availability and can result in a functional response to resource abundance, where preferences change as a function of availability. For generalist species, which utilize a wide range of habitats and resources, quantifying habitat selection is particularly challenging. Spatial and temporal changes in resource abundance can result in changes in selection preference aff ecting the robustness of habitat selection models. We examined selection preference across a wide range of ecological conditions for a generalist megaherbivore, the African savanna elephant Loxodonta africana , to quantify general patterns in selection and to illustrate the importance of functional responses in elephant habitat selection. We found a functional response in habitat selection across both space and time for tree cover, with tree cover being unimportant to habitat selection in the mesic, eastern populations during the wet season. A temporal functional response for water was also evident, with greater variability in selection during the wet season. Selection for low slopes, high tree cover, and far distance from people was consistent across populations; however, variability in selection coeffi cients changed as a function of the abundance of a given resource within the home range. Th is variability of selection coeffi cients could be used to improve confi dence estimations for inferences drawn from habitat selection models. Quantifying functional responses in habitat selection is one way to better predict how wildlife will respond to an ever-changing environment, and they provide promising insights into the habitat selection of generalist species.

Empirical models of habitat selection are increasingly used to guide and inform habitat-based management plans for wildlife species. However, habitat selection does not necessarily equate to habitat quality particularly if selection is maladaptive, so incorporating measures of fitness into estimations of occurrence is necessary to increase model robustness. Here, we incorporated spatially explicit mortality events with the habitat selection of elephants to predict secure and risky habitats in northern Botswana. Following a two-step approach, we first predict the relative probability of use and the relative probability of mortality based on landscape features using logistic regression models. 2Combining these two indices, we then identified low mortality and high use (primary habitat) and areas of high mortality and high use (primary risk). We found that mortalities of adult elephants were closely associated with anthropogenic features, with 80% of mortalities occurring within 25 km of people.Conversely, elephant habitat selection was highest at distances of 30 to 50 km from people. Primary habitat for elephants occurred in the central portion of the study area and within the Okavango Delta; whereas risky areas occurred along the periphery near humans. The protected designation of an area had less influence on the proportion of prime habitat therein than did the locations of the area in relation to human development. Elephant management in southern Africa is moving towards a more self-sustaining, habitat-based approach, and information on selection and mortality could serve as a baseline to help identify demographic sources and sinks to stabilize elephant demography.